Modeling flexural overstrength factor for steel beams using heuristic soft-computing methods

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doi:10.1016/j.istruc.2021.09.075

Modeling flexural overstrength factor for steel beams using heuristic soft-computing methods

2021-12-01

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DOI：10.1016/j.istruc.2021.09.075

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When evaluating the structural performance of steel moment-resisting frames, significant consideration is given to the flexural behavior of steel beams. This analysis focuses on the primary response parameters, such as the ultimate flexural resistance and rotation capacity that dictate the behavior of the steel beam. In practice, the flexural behavior of steel beams determines the ductile design of steel structures, and a sufficient ductility of steel permits plastic hinge rotations until a full-collapse mechanism is attained. However, it is relatively impossible to guarantee the occurrence of a full-collapse mechanism if the beam's flexural overstrength is not appropriately quantified by the application of capacity design procedures. Therefore, this study developed analytical formulations to predict the steel beam's flexural overstrength factor (s) using the multilayer perceptron (MLP)(1), radialbasis-function-based neural network (RBNN), least-squares support vector regression (LSSVR), adaptive neuro fuzzy system with particle swarm optimization (ANFIS-PSO), multivariate adaptive regression splines (MARS), M5 model tree (M5 Tree), and multiple linear regression (MLR). Numerous cross-sectional geometries were tested with square and rectangular hollow sections, and with I and H sections. The data used for the analysis were sourced from completed experimental studies available in the open literature. The data were categorized as training and testing datasets to develop the models based on the aforementioned methods. For the models, the mechanical properties of the materials, shear length of steel beams, and the geometric properties of the sections constituted the independent variables. Overall, the RBNN demonstrated the best prediction of the steel beam overstrength with a root-mean-square error of 0.099 mm, mean absolute error of 0.066 mm, and a correlation coefficient of 0.825. It was observed that all six machine learning methods provided better estimates than the MLR. The machine learning models developed in this study are expected to serve as a hand tool for engineers and steel structure constructors.

Keywords:

Modeling

Flexural overstrength

Database

Steel beams

Soft computing

Modeling flexural overstrength factor for steel beams using heuristic soft-computing methods

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